NREL is a national laboratory of the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.

Connectivity-Enhanced Route Selection

and Adaptive Control for the Chevrolet Volt

2013 SAE Energy Management Symposium: Connectivity Trends 

October 22, 2013

Dearborn Heights, Michigan

Cooperative Research and Development Agreement (CRADA)

between the National Renewable Energy Laboratory (NREL)

and General Motors (GM)

Eric Wood, NREL (presenting)

Jeffrey Gonder, NREL

Sai Rajagopalan, GM

NREL IG 18563

NREL/PR-5400-60543
2

Project Scope

• Intelligent transportation systems (ITS) offer the 

potential for efficiency improvement without 

requiring additional powertrain hardware

• These improvements are most relevant to dual fuel 

powertrains, which have the capability to 

intelligently sequence energy sources and optimize 

hybrid control strategies

• This analysis uses the Chevy Volt as a platform to 

develop/investigate green routing and routeconnected control techniques
3

Proposed Methodology

Drive 

Cycle 

Model

Volt PT 

Model

Road Type

Real-time Traffic

Driver Aggression

Cycle Metrics

Road Grade

Vehicle State

Estimated 

Energy Use

Candidate

Routes

NREL/GM Algorithms

• Computationally heavy to develop

o Hundreds of thousands of drive cycles processed, 

analyzed, and simulated

• Computationally light to implement in-vehicle

o Does not require determination of time/speed trace or 

real-time simulation of high-fidelity vehicle model
4

Outline

• Route-Connected Energy Prediction

o Drive Cycle Prediction

o Energy Estimation Model

• Model Applications

o TSDC + Google Directions API

o Green Routing

o Control Mode Scheduling

TSDC – Transportation Secure Data Center

API – Application Programming Interface
Drive Cycle Prediction
6

Drive Cycle Prediction

Microtrips Nanotrips

• Use historical speed data from TSDC to 

determine how road type affects driving style

• To facilitate application across various models, 

data are binned into equivalent micro-/nano- trips (as shown below)

• Nanotrip bins sized to sufficiently track battery 

state of charge

~0.1 mi bins
7

Drive Cycle Prediction

• Define 2D space with 

average vehicle speed 

and acceleration

• Loop through large 

dataset of real-world 

speed data to populate 

density plots of this 

space
8

High speed 

cruising Hard acceleration

Hard deceleration

Drive Cycle Prediction

• Employ GPS data from 

Atlanta Regional 

Commission Travel 

Survey

• 1,601 unique vehicles

• 290,000 miles and 

8,270 hours of driving 

data

• On a per-distance basis, 

the heaviest 

concentration of data 

exists at high-speed 

cruising conditions from 

40-75 mph
9

Functional Class 1

Functional Class 2

Functional Class 3

Functional Class 4

Functional Class 5

TSDC GPS data are 

map-matched to road 

network to determine 

roads used during 

each leg of trip

Drive Cycle Prediction

Google Maps

NAVTEQ/Nokia/HERE 

road layers
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Drive Cycle Prediction
11

Drive Cycle Prediction

• Isolate data to only look at 

travel on Functional Class 1 

roads

• Data consist mainly of high 

speed-cruising

• Access to real-time traffic 

data would further bolster 

predictions in a production 

environment

Low-speed data on FC1 

assumed to be symptomatic 

of real-time road conditions 

(congestion, weather, etc.)
12

Drive Cycle Prediction

• Now bin data by 

Functional Class AND

previous Functional Class

• Note concentration of 

data around hard 

acceleration when 

transitioning to higher 

speed/capacity roadway

• Combination of current 

state with previous state 

significantly strengthens 

drive cycle prediction 

methods

Near exclusive existence of 

data at positive acceleration
13

Appending Elevation Data

Telemetry Data

Digital Elevation Model

Smooth & Filter Data

*US Geological Survey

• Elevation data are appended to each 

route to improve energy predictions

• Leverage USGS* Digital Elevation Model

• Raw elevations are filtered and 

smoothed to eliminate errant points and 

reduce noise in grade 

calculations
Energy Estimation Model
15

Modeling Environments

• Use GM internal model of Volt to train lookup tables for electric and fuel 

depletion rates (relative to speed, acceleration, grade, and SOC)

• Spot check both models against test data collected from Volt during 

dynamometer testing

Credit: E. Wood (2013)

OEM = Original Equipment Manufacturer
16

All-Electric Operation

• OEM model simulations are aggregated by 

nanotrip and scattered versus speed/accel

• Digital lookup table is then built using data 

from OEM model

• Lookup table is exercised over original data to 

demonstrate degree of fit

Simulated Trained Predictions

RMS Error

11.2%
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Hybrid Operation

• OEM model simulations are aggregated by 

nanotrip and scattered versus speed/accel/SOC

• Digital lookup table is then built using data from 

OEM model

• Lookup table is exercised over original data to 

demonstrate degree of fit

Simulated Trained Predictions

RMS Error

35.9%
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Hybrid Operation

• OEM model simulations are aggregated by 

nanotrip and scattered versus electric rate 

prediction error

• Best fit polynomial is then calculated using 

data from OEM model

• Lookup table is exercised over original data to 

demonstrate degree of fit

X-axis represents difference in electric rates 

predicted by hybrid and all-electric lookup tables

RMS Error

24.8%
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Electric Rate at Grade

• OEM model simulations are aggregated by 

speed/accel and scattered versus road grade

• Digital lookup table is then built using data from 

OEM model

• Lookup table is exercised over original data to 

demonstrate degree of fit

Simulated Trained Predictions

RMS Error

6.5%
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Validation to Test Data

All-electric operation 

@ 0% Grade

• Small amount of 

testing was conducted 

to spot check modeled 

values with respect to 

speed, accel, SOC, and 

road grade

Credit: E. Wood (2013)
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Putting Everything Together...

• Drive cycle and vehicle maps can be overlaid 

to calculate weighted average rate for any 

combination of road type and vehicle state
TSDC + Google Directions API
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5

Model Evaluation: Setting the table

1. Extract real-world 

origin/destination (O/D) 

pairs from NREL’s TSDC

2. Feed O/D pairs through 

Google Directions API 

returning multiple routes 

for each O/D pair

3. Map match routes to 

Navteq streets layer to 

determine functional class 

and speed category

4. Append elevation to each 

route using the USGS 

digital elevation model

5. Filter and smooth 

elevation profiles to 

remove anomalous road 

grades

1

2

4

3

Google Developers API

NAVTEQ/Nokia/HERE

USGS
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Model Evaluation: Example

Elec/fuel rates 

function of 

current and 

previous road 

types, grade, 

and SOC

O

D
Green Routing
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Google Directions

42k O/D Pairs

102k Unique Routes
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Route Summary

• One Option

o Directions API only provided one 

route for 21% of O/D pairs

• Fast = Green

o The route with the estimated shortest 

travel time also required the least 

amount of estimated energy to 

complete for 42% of O/D pairs

• Potential

o The remaining 37% of O/D pairs offer 

the potential for a green routing 

algorithm to inform an energy saving 

route selection

21%

42%

37%

*Consider the route with the shortest 

estimated travel time to be the default.
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Green Routing Example

O

*Normalized Values

D

Route A B C

Distance, mi 81.6 76.2 67.6

Duration, min 107 107 113

Avg Elec Rate, 

Wh/mi* 0.83 0.89 1.0

Avg MPG* 0.45 0.50 1.0

Cost, $* 1.0 0.89 0.59
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Green Routing Example

O

D

*Normalized Values

Route A B C

Distance, mi 65.3 82.6 73.5

Duration, min 101 100 103

Avg Elec Rate, 

Wh/mi* 1.0 0.77 0.88

Avg MPG* 1.0 0.57 0.84

Cost, $* 0.61 1.0 0.72 
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Will people select the green route?

• On aggregate the benefits of 

green routing appear to be 

significant

• However, in order for green 

routes to be selected in 

practice, the incentive of energy 

savings must be weighed 

against disincentive of longer 

travel times
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Value of Time Considerations

• Energy savings is plotted against 

increase in travel time (selecting 

green route over shortest duration)

• This 2D space is swept versus value 

of passenger time ($/hr) to show 

cumulative effects of green routing 

given a return on investment 

threshold

o Ex1: If passenger has no value of 

time, cost/energy could be 

decreased by 12.3% and travel time 

increased by 14.4%

o Ex2: If passenger values time at 

$35/hr, cost/energy could be 

decreased by 1.0% and travel time 

increased by 0.0%

Incur 20 minute 

longer travel time to 

save $3.00?

*The data only consider the 37% of O/D pairs 

previously identified as having green routing potential.
Control Mode Scheduling
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1. Evaluate route twice, assuming:

a) Vehicle operates with no knowledge of route (nominal schedule)

b) Vehicle operates exclusively in AE mode across all links (optimized schedule)

2. Substitute hybrid links into the optimized schedule until:

3. Order substitutions such that the following cost/benefit 

ratio is minimized:

Mode Scheduling: Methodology

CCCC

BBBBBB

=

FF Fhhhh h m

E EA m − E Ehhhh h m

SS f , o ≥ SS f ,

Decrease battery depletion 

rate while minimizing fuel use

(battery depletion defined as 

positive electric rate)

Nominal 

Control

Optimal 

Control

*AE = All-Electric
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Mode Scheduling: Example

25% Fuel 

Reduction

O

D

Nominal 

Control

Optimal 

Control

Utilize engine at highway speeds

Transition from 

highway to residential 

speeds upon 

approaching coast
35

Mode Scheduling: Example

15% Fuel 

Reduction

Anticipate hill climbs O

D

Nominal 

Control

Optimal 

Control

Depart metro area 

making climb into 

mountains
36

Mode Scheduling: Aggregate Results

• Across 100k+ trips, intelligent 

mode scheduling increased 

efficiency by an average of 3.3%

• Short distance trips exhibited the 

greatest potential for mode 

scheduling

• Further gains could be achieved 

by making route-connected 

information available to lowlevel hybrid control algorithms
37

Summary

• Demonstrated ability to 

model vehicle speed/accel

profiles relative to road type

• Constructed high-level 

powertrain model employing 

cycle metrics and vehicle 

state as inputs

• Applied model using real-

world distribution of O/D 

pairs, demonstrating: o Aggregate energy savings of 

up to 4.6% for green routing 

(relative to passenger value of time)

o Average energy savings of 

3.3% for mode scheduling

NREL IG 18563
38
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NAVTEQ/Nokia/HERE Road Attributes

• Speed Category (1-8)

1. Greater than 80 mph 

2. 65-80 mph 

3. 55-64 mph 

4. 41-54 mph 

5. 31-40 mph 

6. 21-30 mph 

7. 6-20 mph 

8. Less than 6 mph 

• Functional Class (1-5)

1. Roads with very few, if any speed changes, typically controlled access, and provide high volume, 

maximum speed movement between and through major metropolitan areas. 

2. Roads with very few, if any speed changes, and provide high volume, high speed traffic 

movement. Typically used to channel traffic to (and from) Level 1 roads. 

3. Roads which interconnect Level 2 roads and provide a high volume of traffic movement at a 

lower level of mobility than Level 2 roads. 

4. Roads that provide for a high volume of traffic movement at moderate speeds between 

neighborhoods. 

5. All other roads.